针对作物品种表现的分析缺少对空间和时间因素的考虑，难以揭示环境效应、互作效应的空间规律，更不能将品种在测试点的表现外推到目标推广环境。因此，该文结合北京金色农华种业公司在黄淮海夏播和东华北春播玉米区的多年多点品种测试数据，以产量为例，提出了一种品种-环境互作效应的空间分析方法。首先，计算和确定了环境效应值、互作效应值的空间渲染分级标准；结合地理数据和玉米种植区划分区结果，利用空间可视化技术初步探究玉米种植环境效应、品种基因-环境互作效应的空间分析方法和分布规律。试验结果表明，该方法可分析品种表现的环境效应和互作效应的空间规律，一区（夏播中晚熟区）环境效应普遍为负，即胁迫较重；二区（春播中熟区）环境效应普遍为正，环境胁迫轻。互作效应的正负反映品种对环境的特殊适应性，其年际和空间波动大小则反映品种的稳定性。生态区一区稳定性：农华101＞郑单958＞先玉335，适应性：郑单958＞农华101＞先玉335。生态区二区稳定性：郑单958＞农华101＞先玉335，适应性：农华101＞郑单958＞先玉335。该文在地理信息系统支持下，对玉米品种互作效应的空间分析方法和分布规律进行初步探究，可为进一步建立作物表现的空间模型提供依据，也可提高品种评价和推广的准确程度。%The performance of crop variety during field production is an emergent property resulting from interactions among genotype effect, environmental effect, and genotype-by-environment interaction (GEI) effect. What’s more, the environmental effect and GEI of crop variety are closely related to the spatial location and the annual fluctuation of the planting environment. However, the current studies are not fully taking the spatial and temporal elements into account. As a result, the spatial distribution pattern of the performance of crop variety can hardly be exploited, and much less be used in the target population environment (TPE). Therefore, this article presents an approach which is based on multi-environment trial (MET) data, including multi-plot for several years in the maize planting region of the east of North China (planting maize in spring) and the Huang-Huai-Hai maize planting region (planting maize in summer) from Beijing Golden Nonghua Seed Science & Technology Company. The yield data cover 3 years (2010-2012) and hundreds of test points and varieties, and we select 3 representative corn varieties (Nonghua 101, Xianyu 335, Zhengdan 958) to analyze. Firstly, the environmental effect and GEI effect are calculated. On the basis of calculation results, the classification standard of spatial color rendering can be determined. Secondly, according to the grade standard of spatial color rendering, we combine the results of environmental effect and GEI effect with the geographic data and the result of maize regionalization, and then visualize them by the spatial visualization tools. Finally, we explore the spatial distribution and spatial analysis approach of environmental effect and GEI effect based on the results of spatial visualization. According to the analysis result of yield data, it turns out that the positive and negative values of environmental effect reflect the seriousness of stress in the planting area. The environmental effect in most regions of seeding in summer and middle-late ripening regions (Area One) is negative, which means the stress in these region is very serious. In the meantime, in the regions of seeding in spring and middle ripening (Area Two), most of the values are positive and the stress is less serious. What’s more, based on earlier finds, the positive and negative values of GEI reflect the suitability between typical planting environment and crop variety, and the annual and spatial fluctuations of GEI reflect the stability of crop variety as well. According to the results, the stability of the 3 maize varieties in Area One is as follow: Nonghua 101 > Zhengdan 958 > Xianyu 335, and the suitability is Zhengdan 958 > Nonghua 101 > Xianyu 335. By contrast, the stability of Area Two shows Zhengdan 958 > Nonghua 101 > Xianyu 335, and the suitability is Nonghua 101 > Zhengdan 958 > Xianyu 335. The experiment results show that the method can analyze the spatial distribution of the performance of crop variety during field production effectively. In conclusion, based on geographic information system (GIS), this article has exploited the spatial distribution and analysis method of environmental effect and GEI of maize, which is not only helping for developing the spatial model of crop field performance and predicting the phenotype of crop variety precisely, but also can be used to improve the accuracy of evaluation in variety.
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